{Tianyu Hong and Peng Wang Chenghuan Jiang Gongrui Zhang Wei Zhou Shuxin Wang Xinhe Li}
Abstract
This paper presents the results of KGCODE-Tab in the tabular data to knowledge graph matching contest SemTab 2022. As an efficient tabular data linking system, KGCODE-Tab is intended to participate in three tasks of the content: Column Type Annotation (CTA), Cell Entity Annotation (CEA), and Columns Property Annotation (CPA). The specific techniques used by KGCODE-Tab will be introduced briefly. The strengths and weaknesses of KGCODE-Tab will also be discussed.
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| cell-entity-annotation-on-biodivtab | KGCODE-Tab | F1 (%): 91.1 |
| cell-entity-annotation-on-toughtables-dbp | KGCODE-Tab | F1 (%): 82.7 |
| column-type-annotation-on-biodivtab | KGCODE-Tab | F1 (%): 86.7 |
| column-type-annotation-on-gittables-semtab | KGCODE-Tab | F1 (%): 58.7 |
| column-type-annotation-on-gittables-semtab-1 | KGCODE-Tab | F1 (%): 69.3 |
| column-type-annotation-on-toughtables-dbp | KGCODE-Tab | F1 (%): 48 |
| column-type-annotation-on-toughtables-wd | KGCODE-Tab | F1 (%): 54.3 |
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